IDEAS home Printed from https://ideas.repec.org/a/eee/agiwat/v316y2025ics0378377425002379.html

Drought resistance cotton genotypes evaluation with multi-year & multi-site study

Author

Listed:
  • Li, Jinglin
  • Liu, Shaodong
  • Liu, Ruihua
  • Ma, Huijuan
  • Shen, Qian
  • Zhang, Siping
  • Ge, Changwei
  • Pang, Chaoyou

Abstract

To determine the agronomic traits closely related to the drought resistance of cotton, and the universal genotypes for drought resistance in cotton. The experiment was conducted in Alaer, Xinjiang Uygur Autonomous and Dunhuang, Gansu Province between 2020 and 2021. 199 cotton genotypes were selected, six agronomic traits: plant height (PH), boll number (BN), single boll weight (SBW), lint percentage (LP), first vegetative shoot length (FVSL), seed cotton yield (SCY) were measured and analyzed. Principal component analysis (PCA) and correlation analysis were conducted on the basis of drought resistance coefficient (DC) value of each agronomic trait. The comprehensive drought coefficient (CDC), drought resistance comprehensive evaluation values (D), and weight drought resistance coefficient (WDC) values were then calculated, and multiple regression analysis was performed with the DC value as the independent variable, the CDC, D, and WDC values serving as dependent variables. Cluster analysis was conducted on the basis of the CDC, D, and WDC values. The results from Alaer and Dunhuang indicated, drought stress significantly reduced the growth of all six agronomic traits. The degree of variation between the two sites varied greatly, indicating that environmental factors affected the response of agronomic traits to drought stress. Correlation analysis revealed that there were significant differences in each agronomic trait correlation between the two sites. Principal component analysis (PCA) indicated that PH, SBW and SCY were stable across both sites, but LP and FVSL were sensitive to environment. Multivariate analysis indicated that compared with CDC and WDC values, D value objectively reflects the contribution of different agronomic traits to drought resistance, with significant differences in the equation coefficients of the six agronomic traits between the two sites. Cluster analysis grouped the 199 cotton genotypes into four groups: high drought resistance, drought resistance, drought sensitive and high drought sensitive, with different allocations at the two sites. Most genotypes exhibited significant differences in drought resistance across the two sites, just seven genotypes: UC016, UC032, UC067, UC073, UC135, UC154 and UC173, presented consistent and universal drought resistance. Among these, UC067, UC154, and UC173 presented relatively high yields and could be used as high-yield and high-drought resistant genotypes.

Suggested Citation

  • Li, Jinglin & Liu, Shaodong & Liu, Ruihua & Ma, Huijuan & Shen, Qian & Zhang, Siping & Ge, Changwei & Pang, Chaoyou, 2025. "Drought resistance cotton genotypes evaluation with multi-year & multi-site study," Agricultural Water Management, Elsevier, vol. 316(C).
    • Handle: RePEc:eee:agiwat:v:316:y:2025:i:c:s0378377425002379
    DOI: 10.1016/j.agwat.2025.109523
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378377425002379
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.agwat.2025.109523?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Puangbut, Darunee & Jogloy, Sanun & Vorasoot, Nimitr & Songsri, Patcharin, 2022. "Photosynthetic and physiological responses to drought of Jerusalem artichoke genotypes differing in drought resistance," Agricultural Water Management, Elsevier, vol. 259(C).
    2. Aiguo Dai, 2011. "Drought under global warming: a review," Wiley Interdisciplinary Reviews: Climate Change, John Wiley & Sons, vol. 2(1), pages 45-65, January.
    3. Corey Lesk & Pedram Rowhani & Navin Ramankutty, 2016. "Influence of extreme weather disasters on global crop production," Nature, Nature, vol. 529(7584), pages 84-87, January.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Huynh, Thanh D. & Nguyen, Thu Ha & Truong, Cameron, 2020. "Climate risk: The price of drought," Journal of Corporate Finance, Elsevier, vol. 65(C).
    2. Javadi, Siamak & Masum, Abdullah-Al, 2021. "The impact of climate change on the cost of bank loans," Journal of Corporate Finance, Elsevier, vol. 69(C).
    3. Guga, Suri & Ma, Yining & Riao, Dao & Zhi, Feng & Xu, Jie & Zhang, Jiquan, 2023. "Drought monitoring of sugarcane and dynamic variation characteristics under global warming: A case study of Guangxi, China," Agricultural Water Management, Elsevier, vol. 275(C).
    4. Li, Ziyue & Zhang, Zhao & Zhang, Lingyan, 2021. "Improving regional wheat drought risk assessment for insurance application by integrating scenario-driven crop model, machine learning, and satellite data," Agricultural Systems, Elsevier, vol. 191(C).
    5. Yuchi Wang & Jiahe Cui & Bailing Miao & Zhiyong Li & Yongli Wang & Chengzhen Jia & Cunzhu Liang, 2024. "Evaluating Performance of Multiple Machine Learning Models for Drought Monitoring: A Case Study of Typical Grassland in Inner Mongolia," Land, MDPI, vol. 13(6), pages 1-20, May.
    6. Michael Nones & Hossein Hamidifar & Seyed Mohammad Bagher Shahabi-Haghighi, 2024. "Exploring EM-DAT for depicting spatiotemporal trends of drought and wildfires and their connections with anthropogenic pressure," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 120(1), pages 957-973, January.
    7. Brown, Kareen & Kim, Sohyung & Lee, Cheol & Pacharn, Parunchana, 2025. "Climate risk and asymmetric cost behavior," Advances in accounting, Elsevier, vol. 68(C).
    8. Wu, Bingfang & Ma, Zonghan & Boken, Vijendra K. & Zeng, Hongwei & Shang, Jiali & Igor, Savin & Wang, Jinxia & Yan, Nana, 2022. "Regional differences in the performance of drought mitigation measures in 12 major wheat-growing regions of the world," Agricultural Water Management, Elsevier, vol. 273(C).
    9. Shima Amini & Sofia Johan & Eilnaz Kashefi-Pour & Abdulkadir Mohamed, 2025. "Climate change risk, investor sentiment, and the performance of new entrant firms," Small Business Economics, Springer, vol. 65(4), pages 2539-2566, December.
    10. S. Bajracharya & L. N. Gunawardhana & J. Sirisena & J. Bamunawala & L. Rajapakse & M. G. N. Odara, 2025. "Unlocking the mysteries of drought: integrating snowmelt dynamics into drought analysis at the Narayani River Basin, Nepal," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 121(5), pages 5267-5291, March.
    11. He, Liuyue & Xu, Zhenci & Wang, Sufen & Bao, Jianxia & Fan, Yunfei & Daccache, Andre, 2022. "Optimal crop planting pattern can be harmful to reach carbon neutrality: Evidence from food-energy-water-carbon nexus perspective," Applied Energy, Elsevier, vol. 308(C).
    12. Kedi Liu & Ranran Wang & Inge Schrijver & Rutger Hoekstra, 2024. "Can we project well-being? Towards integral well-being projections in climate models and beyond," Humanities and Social Sciences Communications, Palgrave Macmillan, vol. 11(1), pages 1-11, December.
    13. El-Saied E. Metwaly & Hatim M. Al-Yasi & Esmat F. Ali & Hamada A. Farouk & Saad Farouk, 2022. "Deteriorating Harmful Effects of Drought in Cucumber by Spraying Glycinebetaine," Agriculture, MDPI, vol. 12(12), pages 1-16, December.
    14. Yang Li & Dingwen Zhang & Ying Wen & Xiaoling Liu & Yi Zhang & Guangmei Wang, 2024. "Spatiotemporal Patterns and Driving Factors of Carbon Footprint in Coastal Saline Cropland Ecosystems: A Case Study of the Yellow River Delta, China," Land, MDPI, vol. 13(12), pages 1-18, December.
    15. repec:ags:aaea22:335489 is not listed on IDEAS
    16. repec:plo:pone00:0185481 is not listed on IDEAS
    17. Teerachai Amnuaylojaroen & Pavinee Chanvichit, 2024. "Historical Analysis of the Effects of Drought on Rice and Maize Yields in Southeast Asia," Resources, MDPI, vol. 13(3), pages 1-18, March.
    18. N. Zhang & H. Huang, 2018. "Assessment of world disaster severity processed by Gaussian blur based on large historical data: casualties as an evaluating indicator," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 92(1), pages 173-187, May.
    19. Liu, Zhipeng & Jiao, Xiyun & Zhu, Chengli & Katul, Gabriel G. & Ma, Junyong & Guo, Weihua, 2021. "Micro-climatic and crop responses to micro-sprinkler irrigation," Agricultural Water Management, Elsevier, vol. 243(C).
    20. Teresa Armada Brás & Jonas Jägermeyr & Júlia Seixas, 2019. "Exposure of the EU-28 food imports to extreme weather disasters in exporting countries," Food Security: The Science, Sociology and Economics of Food Production and Access to Food, Springer;The International Society for Plant Pathology, vol. 11(6), pages 1373-1393, December.
    21. Ashenafi Yimam Kassaye & Guangcheng Shao & Xiaojun Wang & Shiqing Wu, 2021. "Quantification of drought severity change in Ethiopia during 1952–2017," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(4), pages 5096-5121, April.
    22. Della Peruta, Raniero & Mereu, Valentina & Spano, Donatella & Marras, Serena & Vezy, Rémi & Trabucco, Antonio, 2025. "Projecting trends of arabica coffee yield under climate change: A process-based modelling study at continental scale," Agricultural Systems, Elsevier, vol. 227(C).

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:agiwat:v:316:y:2025:i:c:s0378377425002379. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/agwat .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.